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Practical differential privacy
Yu-Xiang Wang · Fariba Yousefi
Fri Dec 09 10:00 AM -- 10:15 AM (PST) @
Author Information
Yu-Xiang Wang (UC Santa Barbara)
Fariba Yousefi (AstraZeneca)
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2021 : Instance-dependent Offline Reinforcement Learning: From tabular RL to linear MDPs »
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2022 : Generalized PTR: User-Friendly Recipes for Data-Adaptive Algorithms with Differential Privacy »
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2022 : VOTING-BASED APPROACHES FOR DIFFERENTIALLY PRIVATE FEDERATED LEARNING »
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2022 : Offline Reinforcement Learning with Closed-Form Policy Improvement Operators »
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2022 : Offline Policy Evaluation for Reinforcement Learning with Adaptively Collected Data »
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2022 : Near-Optimal Deployment Efficiency in Reward-Free Reinforcement Learning with Linear Function Approximation »
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2022 : Differentially Private Gradient Boosting on Linear Learners for Tabular Data »
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2022 : Differentially Private Bias-Term only Fine-tuning of Foundation Models »
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2023 Poster: Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger »
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2023 Poster: Offline Reinforcement Learning with Differential Privacy »
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2023 Poster: Posterior Sampling with Delayed Feedback for Reinforcement Learning with Linear Function Approximation »
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2023 Poster: Online Label Shift: Optimal Dynamic Regret meets Practical Algorithms »
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2023 Poster: Improving the Privacy and Practicality of Objective Perturbation for Differentially Private Linear Learners »
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2023 Poster: A Privacy-Friendly Approach to Data Valuation »
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2022 : Contributed Talk: Differentially Private Bias-Term only Fine-tuning of Foundation Models »
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2022 : Panel on Privacy and Security in Machine Learning Systems »
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2022 : Practical differential privacy »
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2022 Workshop: Challenges in Deploying and Monitoring Machine Learning Systems »
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2022 Poster: SeqPATE: Differentially Private Text Generation via Knowledge Distillation »
Zhiliang Tian · Yingxiu Zhao · Ziyue Huang · Yu-Xiang Wang · Nevin L. Zhang · He He -
2022 Poster: Differentially Private Linear Sketches: Efficient Implementations and Applications »
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2022 Poster: Optimal Dynamic Regret in LQR Control »
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2021 Workshop: Privacy in Machine Learning (PriML) 2021 »
Yu-Xiang Wang · Borja Balle · Giovanni Cherubin · Kamalika Chaudhuri · Antti Honkela · Jonathan Lebensold · Casey Meehan · Mi Jung Park · Adrian Weller · Yuqing Zhu -
2020 Workshop: Privacy Preserving Machine Learning - PriML and PPML Joint Edition »
Borja Balle · James Bell · Aurélien Bellet · Kamalika Chaudhuri · Adria Gascon · Antti Honkela · Antti Koskela · Casey Meehan · Olga Ohrimenko · Mi Jung Park · Mariana Raykova · Mary Anne Smart · Yu-Xiang Wang · Adrian Weller -
2019 Poster: Multi-task Learning for Aggregated Data using Gaussian Processes »
Fariba Yousefi · Michael T Smith · Mauricio Álvarez